👋 Hi, I’m Keerthi Kumaran

🎓 PhD Candidate in Physics @ Purdue University
🔬 Quantum Computing: Simulations | Error Mitigation | QEC

Website | GitHub | LinkedIn | Google Scholar

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🗺️ Quick Navigation

About Me | Research Vision | Applications (Simulations & QML) | Foundations (QEC) | Achievements

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🧠 About Me

I’m a PhD student at Purdue specializing in quantum computing. My work bridges the gap between theoretical physics and experimental quantum hardware.

• Experience: I’ve contributed to high-impact projects at IBM Quantum (Yorktown Heights) as a Summer Research Intern (2024 & 2025), focusing on Sample-Based Quantum Diagonalization and utility-scale experiments.
• Core Passion: Pushing the boundaries of quantum technology through interdisciplinary collaboration and open-source development.

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🚀 The Vision: Quantum Advantage & Utility

My research is driven by the transition from NISQ experiments to Quantum Utility—where quantum processors provide reliable results for problems classically out of reach.

• Quantum Advantage: Achieving exponential speedups via high-dimensional Hilbert spaces.
• Quantum Utility: Utilizing error-mitigated hardware (100+ qubits) to simulate physics (e.g., Heisenberg chains) where exact classical methods begin to struggle.

Upcoming work: Developing utility-scale benchmarks for many-body dynamics.

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🔬 Applications

⚛️ Quantum Simulations (Core Research)

Mapping complex Hamiltonians to quantum hardware to study topological phases and transport.

• Spin Systems & Qutrits: Simulation of AKLT Systems using calibrated superconducting transmons.
  📄 arXiv:2412.19786 | 💻 View Code
• Dynamics: Superdiffusion Breakdown in 2D Heisenberg Chains and Chiral Edge Dynamics on Trapped Ions.
  📄 arXiv:2503.14371 | 📄 arXiv:2507.08939
• Chemistry: Simulation of Resonating Valence Bond (RVB) states.
  📄 J. Phys. Chem. A (2023)

🤖 Quantum Machine Learning (QML)

Exploring how quantum circuits can compress and process high-dimensional data.

• Random Projection & Dimensionality Reduction: Benchmarking quantum vs. classical PCA using random circuits.
  📄 Phys. Rev. Research (2024)

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🛡️ What Makes Advantage Possible?

Quantum advantage requires more than just more qubits; it requires Error Correction and Suppression.

• Error Mitigation: Using Zero-Noise Extrapolation (ZNE) and Pauli-Twirling in noisy 2D Heisenberg simulations.
• Quantum Error Correction (QEC): Researching Topological QEC schemes and simulating non-Abelian anyons for fault-tolerant hardware.
• Tools: Qiskit Runtime, hardware-aware circuit optimization, and noise characterization.

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🏆 Achievements

- 🎖️ **Advanced Badge**, IBM Fall Quantum Challenge - 🇩🇪 **DAAD WISE Scholar**, Germany - 🧑‍🏫 **Reviewer**, *Journal of Physics A* - 🧑‍💻 **Top 15%**, QHack 2023 (Xanadu)

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